Research graph
References from Development of an Interpretable Triage Tool for Colorectal Polyp Risk Stratification Within a Population-Based Screening Program: Machine Learning Approach. Local targets link to admitted publications; unresolved targets remain external evidence.
Global burden of colorectal cancer in 2020 and 2040: incidence and mortality estimates from GLOBOCAN
10.1136/gutjnl-2022-327736 · External reference
Prevalence of diverse colorectal polyps and risk factors for colorectal carcinoma in situ and neoplastic polyps
10.1186/s12967-024-05111-z · External reference
Proteomic profiling of colorectal adenomas identifies a predictive risk signature for development of metachronous advanced colorectal neoplasia
10.1053/j.gastro.2023.03.208 · External reference
The global, regional, and national burden of colorectal cancer and its attributable risk factors in 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017
10.1016/s2468-1253(19)30345-0 · External reference
Novel colorectal cancer screening methods—opportunities and challenges
10.1038/s41571-025-01037-7 · External reference
Identification of diagnostic biomarkers for colorectal polyps based on noninvasive urinary metabolite screening and construction of a nomogram
10.1002/cam4.70762 · External reference
CT colonography versus multitarget stool DNA test for colorectal cancer screening: a cost-effectiveness analysis
10.1148/radiol.243775 · External reference
A comprehensive study on colorectal polyp segmentation with ResUNet++, conditional random field and test-time augmentation
10.1109/jbhi.2021.3049304 · External reference
Colorectal cancer screening: recommendations for physicians and patients from the U.S. Multi-Society Task Force on Colorectal Cancer
10.1053/j.gastro.2017.05.013 · External reference
ASGE review of adverse events in colonoscopy
10.1016/j.gie.2019.07.033 · External reference
Meat intake, heterocyclic amine exposure, and metabolizing enzyme polymorphisms in relation to colorectal polyp risk
10.1158/1055-9965.epi-07-0615 · External reference
Metabolism and colorectal cancer
10.1146/annurev-pathmechdis-031521-041113 · External reference
Association between risk factors for colorectal cancer and risk of serrated polyps and conventional adenomas
10.1053/j.gastro.2018.04.019 · External reference
Development and multi-center validation of a machine learning model for advanced colorectal neoplasms screening
10.1016/j.compbiomed.2025.110066 · External reference
Development and validation of machine learning algorithms for prediction of colorectal polyps based on electronic health records
10.3390/biomedicines12091955 · External reference
Development and validation of a lifestyle-based 10-year risk prediction model of colorectal cancer for early stratification: evidence from a longitudinal screening cohort in China
10.3390/nu17111898 · External reference
Machine learning reveals the most important psychological and social variables predicting the differential diagnosis of rheumatic and musculoskeletal diseases
10.1007/s00296-021-04916-1 · External reference
Cost and cost-effectiveness of the colorectal cancer screening program for key populations in Zhejiang Province, 2020-2022 [Article in Chinese]
10.3760/cma.j.cn112338-20240918-00581 · External reference
Epidemiology and risk factors of colorectal polyps
10.1016/j.bpg.2017.06.004 · External reference
Risk factors for colorectal polyps and cancer
10.1016/j.giec.2021.12.008 · External reference
Heart disease prediction using supervised machine learning algorithms: performance analysis and comparison
10.1016/j.compbiomed.2021.104672 · External reference
SMOTE for learning from imbalanced data: progress and challenges, marking the 15-year anniversary
10.1613/jair.1.11192 · External reference
A population-based study exploring phenotypic clusters and clinical outcomes in stroke using unsupervised machine learning approach
10.1371/journal.pdig.0000334 · External reference
A quick review of machine learning algorithms
10.1109/comitcon.2019.8862451 · External reference
10.1007/978-1-4899-7641-3_9
10.1007/978-1-4899-7641-3_9 · External reference
Comparing different supervised machine learning algorithms for disease prediction
10.1186/s12911-019-1004-8 · External reference
Decision tree methods: applications for classification and prediction
10.11919/j.issn.1002-0829.215044 · External reference
Early prediction of diabetes using an ensemble of machine learning models
10.3390/ijerph191912378 · External reference
Prediction of postpartum depression in women: development and validation of multiple machine learning models
10.1186/s12967-025-06289-6 · External reference
Application of explainable artificial intelligence in medical health: a systematic review of interpretability methods
10.1016/j.imu.2023.101286 · External reference
Local interpretable model-agnostic explanations for classification of lymph node metastases
10.3390/s19132969 · External reference
Unresolved reference
External reference
Predictive value of genetic risk scores in the development of colorectal adenomas
10.1007/s10620-021-07218-5 · External reference
Establishment of clinical predictive model based on the study of influence factors in patients with colorectal polyps
10.3389/fsurg.2023.1077175 · External reference
Simple prediction model for colorectal serrated polyps: development and external validation study in U.S. prospective cohorts
10.1158/1940-6207.capr-22-0335 · External reference
Screening and overdiagnosis: public health implications
10.1186/s40985-015-0012-1 · External reference
Colorectal polyps: stand-alone performance of computer-aided detection in a large asymptomatic screening population
10.1148/radiol.10092292 · External reference
Accuracy of artificial intelligence on histology prediction and detection of colorectal polyps: a systematic review and meta-analysis
10.1016/j.gie.2020.02.033 · External reference
Automatic detection and classification of colorectal polyps by transferring low-level CNN features from nonmedical domain
10.1109/jbhi.2016.2635662 · External reference
Screening and prevention of colorectal cancer
10.1136/bmj.n1855 · External reference
Screening and surveillance of colorectal cancer: a review of the literature
10.3390/cancers16152746 · External reference
Cigarette smoking and adenomatous polyps: a meta-analysis
10.1053/j.gastro.2007.11.007 · External reference
Tobacco smoking and colorectal hyperplastic and adenomatous polyps
10.1158/1055-9965.epi-05-0883 · External reference
Lifestyle risk factors for serrated colorectal polyps: a systematic review and meta-analysis
10.1053/j.gastro.2016.09.003 · External reference
Modifiable lifestyle factors associated with risk of sessile serrated polyps, conventional adenomas and hyperplastic polyps
10.1136/gutjnl-2016-312893 · External reference
Serrated and non-serrated polyps of the colorectum: their prevalence in an unselected case series and correlation of BRAF mutation analysis with the diagnosis of sessile serrated adenoma
10.1136/jcp.2008.061960 · External reference
Male gender is a risk factor for high-risk colorectal polyps
10.4328/acam.21739 · External reference
Trend of the polyp and adenoma detection rate by sex and age in asymptomatic average-risk and high-risk individuals undergoing screening colonoscopy, 2012-2019
10.1016/j.pmedr.2023.102468 · External reference
Sex differences in colorectal cancer: with a focus on sex hormone-gut microbiome axis
10.1186/s12964-024-01549-2 · External reference
Sex differences in associations among metabolic syndrome, obesity, related biomarkers, and colorectal adenomatous polyp risk in a Japanese population
10.3164/jcbn.18-11 · External reference
Risk of colorectal cancer in first degree relatives of patients with colorectal polyps: nationwide case-control study in Sweden
10.1136/bmj.n877 · External reference
Update on genetic predisposition to colorectal cancer and polyposis
10.1016/j.mam.2019.03.001 · External reference
Colorectal cancer and central obesity
10.1001/jamanetworkopen.2024.54753 · External reference
Independent and joint associations of general and abdominal obesity with the risk of conventional adenomas and serrated polyps: a large population-based study in East Asia
10.1002/ijc.34503 · External reference
BMI and the risk of colorectal adenoma in African-Americans
10.1002/oby.20702 · External reference
Overweight and obesity are associated with colorectal neoplasia in an Australian outpatient population
10.1038/s41598-024-74042-y · External reference
Obesity and incidence of colorectal polyps: a case-controlled study
10.1097/ms9.0000000000000234 · External reference
Obesity and inflammation: colorectal cancer engines
10.2174/1874467214666210906122054 · External reference
Metabolic inflammation and insulin resistance in obesity
10.1161/circresaha.119.315896 · External reference
Body fatness over the life course and risk of serrated polyps and conventional adenomas
10.1002/ijc.32958 · External reference
Association of Fusobacterium nucleatum with clinical and molecular features in colorectal serrated pathway
10.1002/ijc.29488 · External reference
Occurrence of colorectal adenomas in younger adults: an epidemiologic necropsy study
10.1016/j.cgh.2008.03.022 · External reference
Autocatalytic tissue polymerization reaction mechanism in colorectal cancer development and growth
10.3390/cancers12020460 · External reference
Attributable causes of colorectal cancer in China
10.1186/s12885-017-3968-z · External reference
Unresolved reference
External reference
